Abstract
Using the boxcar representation in the spatial domain and a signal-space representation of its frequency-weighted k-space, an iterative prediction method is developed to derive an improved low-resolution phase approximation for phase correction. Compared to the homodyne filter, the proposed predictor is found to be more efficient due to its capability of exhibiting an equivalent degree of performance using a lower number of fractional lines. The phase correction performance is illustrated using partially acquired susceptibility weighted images (SWI). An extension of the predictor into higher frequency regions of phase-encodes in conjunction with a signal-space projection in the frequency-weighted partial k-space is shown to provide restoration of fine structural details of sparse magnitude images. The application of subspace projection filtering is demonstrated using magnetic resonance angiogram (MRA).
Highlights
Phase errors in MRI can result from off resonance effects due to imperfections of the static magnetic field or significant changes in the susceptibility within the imaged field-ofview (FOV) [1,2,3,4]
With the acquired data consisting of the entire set of positive phase-encodes, the region of this symmetric phase-encode coverage includes the set of fractional lines in the negative phase-encode region
This work presents FIR filters for reduction of artifacts and phase errors resulting from truncation of conjugate asymmetric k-space
Summary
Phase errors in MRI can result from off resonance effects due to imperfections of the static magnetic field or significant changes in the susceptibility within the imaged field-ofview (FOV) [1,2,3,4]. The effects can be produced from under-sampling in either phase or frequency-encode directions Often, these artifacts referred to as “Gibbs Ringing” are manifested as striations of sharper image edges in the form of repeated bands parallel to the edge [6]. The solutions for alleviation of phase errors, and simultaneous measures for artifact suppression have been broadly addressed using three distinct approaches These consist of (1) methods based on low-resolution symmetric data, (2) parametric models, and (3) statistical estimation. With the acquired data consisting of the entire set of positive phase-encodes, the region of this symmetric phase-encode coverage includes the set of fractional lines in the negative phase-encode region The phase of this low-resolution data is used for phase correction.
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